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HackerNoon | Learn Any Technology engagement report

@hackernoon - 95K followers on X

Measured over 23 original posts from a 30-day window, last computed on August 25, 2026.

Engagement

Bottom quarter for its size
Per follower
0.005%
of 95K followers
Per impression
0.299%
1.7K views on a typical post
Reach
1.77%
of its followers see a post
Typical post
5
interactions (median)
Saved
0.06%
1 bookmarks on a typical post
Posting rate
2/day
active 23% of days
Peak time
13:00 UTC
Tuesday

A typical post picks up 5 interactions against 95K followers, an engagement rate of 0.005%. Measured over 23 original posts, its engagement rate beats 12% of 4,347 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 1.7K times each, and 0.299% of those impressions turn into an interaction. That is about 1.77% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 23 posts sampled, 26% carry an image or video, 17% are part of a thread and 83% link out. The account's strongest tracked post pulled 549 interactions, about 110x its own typical post. Recurring topics include #aifilmmaking, #aiimplementation, #businessintelligence.

Measured over 23 original posts from a 30-day window, last computed on August 25, 2026. Recurring tags: #aifilmmaking, #aiimplementation, #businessintelligence.

Compared with accounts its own size

HackerNoon | Learn Any Technology's engagement rate beats 12% of the tracked X accounts closest to it in follower count (4,347 accounts, accounts of similar size (decile 5 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 17% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.005%, HackerNoon | Learn Any Technology sits above the 10th percentile of the 42,087 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.013%.

p100.002%
p250.013%
p50 (median)0.084%
p750.449%
p902.07%
p99143.1%
Engagement rate as a share of followers, across the 42,087 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 84,187 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.002%
25th percentile0.013%
50th percentile0.084%
75th percentile0.449%
90th percentile2.07%
99th percentile143.1%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 13:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 13:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 6%Busiest hour: 13:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%59K
01:00 UTC-2%60K
02:00 UTC-4%58K
03:00 UTC-4%62K
04:00 UTC-6%50K
05:00 UTC-5%49K
06:00 UTC-5%56K
07:00 UTC-5%61K
08:00 UTC-4%71K
09:00 UTC-4%82K
10:00 UTC-3%84K
11:00 UTC-3%91K
12:00 UTC-2%100K
13:00 UTC-2%110K
14:00 UTC-3%113K
15:00 UTC-2%117K
16:00 UTC-3%114K
17:00 UTC-3%106K
18:00 UTC-2%99K
19:00 UTC-2%93K
20:00 UTC-1%87K
21:00 UTC-1%77K
22:00 UTC-2%67K
23:00 UTC-1%60K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 5%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%257K
Monday+1%329K
Tuesday-2%355K
Wednesday-4%323K
Thursday-3%273K
Friday-3%277K
Saturday+3%250K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Best tweets

  • Aug 19, 2026110x their median

    Why can yesterday's dashboard numbers change when nobody changed the data? @TimescaleDB explains how late-arriving records, invalidation, and refresh windows affect continuous aggregates—and how to keep historical results consistent: https://t.co/2u3nRYZfuQ

    500391001.1M viewsView on X
  • Aug 21, 2026101x their median

    What if Claude Code could check its own work after every edit? This five-line hook connects Claude Code to SonarQube's Agentic Analysis and feeds code-quality findings back into the development workflow. Read @SonarSource's breakdown: https://t.co/7eOflhmegh

    45340130860K viewsView on X
  • Aug 20, 202666x their median

    AI coding tools generate a lot of output. The format you choose can have a surprisingly large impact on efficiency. Learn from @SonarSource how TOON and a simple Sonar CLI setting can reduce AI coding-agent usage compared with JSON output: https://t.co/yyZ55q1Fu1

    258641001.1M viewsView on X
  • Aug 21, 202665x their median

    AI agents don't just need a model—they need infrastructure to run reliably around the clock. Compare laptops, VPSs, and managed runtimes, and explore the security, deployment, and observability considerations behind always-on agents: https://t.co/D7ktM2Yc3m

    27027260578K viewsView on X
  • Aug 20, 202662x their median

    AI tools are changing how legal teams work, but successful adoption depends on governance, oversight, and clear policies. This article explores practical considerations for using AI in legal workflows: https://t.co/u97RFZBALf

    248541001.1M viewsView on X
  • Aug 20, 202649x their median

    Managing compliance requirements across a supply chain can get complicated. See how SecurityMetrics' CMMC Link is designed to simplify compliance workflows and make requirements easier to manage: https://t.co/oyAAbgvsDz

    182521101.2M viewsView on X
  • Jun 2, 202627x their median

    The Decentralize AI Hackathon is live 💚 For builders, developers, and technical thinkers working on open AI infrastructure: → Enter with a project, prototype, or technical idea, publish your work as a @hackernoon blog post → $51,750+ prize pool and the GRAND PRIZE of the https://t.co/Inw4J5VJKx domain → Free compute credits for every eligible participant → Two rounds: June '26 through Feb '27 → Start at the concept stage. Keep building. Submit updates as your work evolves. Enter today 👉 https://t.co/Inw4J5VJKx Sponsored by @nosana_ai, @ArweaveEco, and @MEXC #DecentralizeAI

    931920130K viewsView on X
  • Aug 24, 202626x their median

    AI coding agents can move fast. Guardrails help keep them on track. Learn from @SonarSource how Claude Code hooks can enforce checks, block unwanted commands, and validate AI-generated code before it ships: https://t.co/HIs8OLzUn4

    1052230383K viewsView on X
  • Aug 19, 202613x their median

    Security teams are dealing with more alerts than ever. The challenge is finding the signals that actually need attention. This case study by @anyrun_app explores how SOC teams can streamline incident investigation and respond to threats more efficiently: https://t.co/1jKfWVk87d

    632103.4K viewsView on X
  • Aug 25, 20266.0x their median

    Learn how QA agents use knowledge graphs to understand your product, test by user goals, adapt to UI changes, and reduce test maintenance: https://t.co/PUPBUv5pOt

    2352061K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

Recurring topics

#aifilmmaking#aiimplementation#businessintelligence#dataanalytics#enterpriseai

The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.

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Reading these numbers

A typical post picks up 5 interactions against 95K followers, an engagement rate of 0.005%. Measured over 23 original posts, its engagement rate beats 12% of 4,347 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 1.7K times each, and 0.299% of those impressions turn into an interaction. That is about 1.77% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 23 posts sampled, 26% carry an image or video, 17% are part of a thread and 83% link out. The account's strongest tracked post pulled 549 interactions, about 110x its own typical post. Recurring topics include #aifilmmaking, #aiimplementation, #businessintelligence.

What is HackerNoon | Learn Any Technology's engagement rate on X?
HackerNoon | Learn Any Technology (@hackernoon) has an engagement rate of 0.005%, based on the median interactions across 23 original posts from the last 30 days against 94,514 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.005%, HackerNoon | Learn Any Technology sits above the 10th percentile of the 42,087 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @hackernoon have real engagement?
Its engagement rate beats 12% of the tracked X accounts closest to it in follower count (4,347 accounts), which puts it in the bottom quarter for its size group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @hackernoon post?
Most posts go out around 13:00 UTC, and Tuesday is its busiest day, at roughly 2 posts per day across the measured window.

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